The rise of Industry 4.0 and smart manufacturing has highlighted the importance of utilizing intelligent manufacturing techniques, tools, and methods, including predictive maintenance. This feature allows for the early identification of potential issues with machinery, preventing them from reaching critical stages. This paper proposes an intelligent predictive maintenance system for industrial equipment monitoring. The system integrates Industrial IoT, MQTT messaging and machine learning algorithms. Vibration, current and temperature sensors collect real-time data from electrical motors which is analyzed using five ML models to detect anomalies and predict failures, enabling proactive maintenance. The MQTT protocol is used for efficient communication between the sensors, gateway devices, and the cloud server. The system was tested on an operational motors dataset, five machine learning algorithms, namely k-nearest neighbor (KNN), supported vector machine (SVM), random forest (RF), linear regression (LR), and naive bayes (NB), are used to analyze and process the collected data to predict motor failures and offer maintenance recommendations. Results demonstrate the random forest model achieves the highest accuracy in failure prediction. The solution minimizes downtime and costs through optimized maintenance schedules and decisions. It represents an Industry 4.0 approach to sustainable smart manufacturing.
Deep learning convolution neural network has been widely used to recognize or classify voice. Various techniques have been used together with convolution neural network to prepare voice data before the training process in developing the classification model. However, not all model can produce good classification accuracy as there are many types of voice or speech. Classification of Arabic alphabet pronunciation is a one of the types of voice and accurate pronunciation is required in the learning of the Qur’an reading. Thus, the technique to process the pronunciation and training of the processed data requires specific approach. To overcome this issue, a method based on padding and deep learning convolution neural network is proposed to
... Show MoreBuildings begin to deteriorate gradually over time due to several factors, including environmental influences, improper use of the building, and neglected repairs for damages during the building's life span. Effective maintenance practices can minimize operational costs, extend the life of building systems and components, improve energy efficiency, and maintain property value. This paper aims to review articles related to building maintenance to identify factors affecting maintenance practices. After conducting the review, the result was that there were 33 factors affecting building maintenance categorized into six groups: management-related factors, manpower-related factors, technical-related factors, financial-
... Show MoreChannel estimation and synchronization are considered the most challenging issues in Orthogonal Frequency Division Multiplexing (OFDM) system. OFDM is highly affected by synchronization errors that cause reduction in subcarriers orthogonality, leading to significant performance degradation. The synchronization errors cause two issues: Symbol Time Offset (STO), which produces inter symbol interference (ISI) and Carrier Frequency Offset (CFO), which results in inter carrier interference (ICI). The aim of the research is to simulate Comb type pilot based channel estimation for OFDM system showing the effect of pilot numbers on the channel estimation performance and propose a modified estimation method for STO with less numb
... Show More<p>Mobility management protocols are very essential in the new research area of Internet of Things (IoT) as the static attributes of nodes are no longer dominant in the current environment. Proxy MIPv6 (PMIPv6) protocol is a network-based mobility management protocol, where the mobility process is relied on the network entities, named, Mobile Access Gateways (MAGs) and Local Mobility Anchor (LMA). PMIPv6 is considered as the most suitable mobility protocol for WSN as it relieves the sensor nodes from participating in the mobility signaling. However, in PMIPv6, a separate signaling is required for each mobile node (MN) registration, which may increase the network signaling overhead and lead to increase the total handoff latency
... Show MoreSphingolipids are key components of eukaryotic membranes, particularly the plasma membrane. The biosynthetic pathway for the formation of these lipid species is largely conserved. However, in contrast to mammals, which produce sphingomyelin, organisms such as the pathogenic fungi and protozoa synthesize inositol phosphorylceramide (IPC) as the primary phosphosphingolipid. The key step involves the reaction of ceramide and phosphatidylinositol catalysed by IPC synthase, an essential enzyme with no mammalian equivalent encoded by the AUR1 gene in yeast and recently identified functional orthologues in the pathogenic kinetoplastid protozoa. As such this enzyme represents a promising target for novel anti-fungal and anti-protozoal drugs. Given
... Show MoreSemantic segmentation realization and understanding is a stringent task not just for computer vision but also in the researches of the sciences of earth, semantic segmentation decompose compound architectures in one elements, the most mutual object in a civil outside or inside senses must classified then reinforced with information meaning of all object, it’s a method for labeling and clustering point cloud automatically. Three dimensions natural scenes classification need a point cloud dataset to representation data format as input, many challenge appeared with working of 3d data like: little number, resolution and accurate of three Dimensional dataset . Deep learning now is the po
A new algorithm is proposed to compress speech signals using wavelet transform and linear predictive coding. Signal compression based on the concept of selecting a small number of approximation coefficients after they are compressed by the wavelet decomposition (Haar and db4) at a suitable chosen level and ignored details coefficients, and then approximation coefficients are windowed by a rectangular window and fed to the linear predictor. Levinson Durbin algorithm is used to compute LP coefficients, reflection coefficients and predictor error. The compress files contain LP coefficients and previous sample. These files are very small in size compared to the size of the original signals. Compression ratio is calculated from the size of th
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